Klarna Bank AB jobb

What you will do Perform independent end-to-end validation of fraud detection ML models, including conceptual soundness, data integrity, feature engineering, model development, deployment design, and monitoring frameworks. Develop challenger models. Review and challenge first-line fraud model methodologies, assumptions, and implementation choices (e.g., scikit-learn, LightGBM, graph models, anonaly detection techniques, GenAI components). Build and deploy agentic AI tools to support model validation workflows — automating review of model documentation and code, surfacing risks and inconsistencies. Assess model performance using appropriate fraud metrics (e.g., precision/recall, ROC-AUC, PR-AUC, cost-sensitive metrics, fraud rate capture, business impact trade-offs). Evaluate model stability, drift detection, retraining strategies, and production monitoring practices. Independently replicate model results where necessary and conduct challenger analyses to assess model robustness and limitations. Review large-scale transaction datasets and feature pipelines (e.g., >100M transactions, hundreds of features) to assess data representativeness, leakage risks, and bias. Evaluate model governance documentation, explainability approaches, and transparency — including regulatory compliance related to model risk, fairness, and data privacy. Validate new technologies applied in fraud detection, such as Graph Networks, Behavioral Biometrics, Anomaly Detection, and GenAI-based systems. Assess controls around CI/CD pipelines, deployment processes (e.g., Docker, Jenkins), and cloud environments (e.g., AWS SageMaker, S3, Athena, Lambda). Develop and maintain validation frameworks, testing standards, and model performance monitoring tools (e.g., SQL, PySpark, Python-based validation libraries). Collaborate closely with first-line fraud data scientists, ML engineers, product, and business stakeholders to ensure transparent communication of model risks and validation findings. Provide actionable recommendations and formally document validation outcomes in line with internal model governance standards and external regulatory expectations. Stay up to date with evolving fraud typologies, emerging ML/AI techniques, and regulatory developments in model risk management. Who you are Advanced degree (Master’s or PhD) in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Physics, or Engineering. 3+ years of hands-on experience in fraud-related modeling (e.g., transaction fraud, account takeover, identity fraud, payments fraud etc). Strong expertise in machine learning methods used in fraud detection, including tree-based models (e.g., LightGBM), anomaly detection, graph/network models, and advanced ML techniques. Deep understanding of the end-to-end ML lifecycle — from conceptual design and feature engineering to production deployment and monitoring — with the ability to critically challenge each stage. Strong programming skills in Python and SQL; experience with PySpark/Spark and large-scale data processing. Experience building agentic AI workflows. Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Lambda, S3, Athena) and production deployment workflows. Strong knowledge of model validation principles, model risk governance frameworks, and regulatory expectations. Experience assessing model bias, fairness, explainability, and privacy risks. Excellent analytical thinking and structured problem-solving skills, with the ability to assess complex models and clearly articulate risks and limitations. Strong communication skills, capable of translating technical findings into clear, actionable insights for senior stakeholders and non-technical audiences. Ability to work independently while constructively challenging first-line teams in a collaborative manner. Awesome to have Experience in BNPL, credit cards, payments, or other transaction-heavy financial products. Experience validating models in highly regulated environments. Experience mentoring junior validators or leading validation reviews. Exposure to inference of rejected transactions and understanding of fraud/credit overlap. Familiarity with AI governance frameworks and emerging AI regulatory requirements.

What you will do Klarna strives to become the world’s favourite way to buy, and you can contribute to reaching this goal! We are looking to hire great people, who are passionate about using their talents to generate success. We are hiring Product Managers at various levels of seniority. During the recruitment process we will evaluate your seniority, your skills and ask about your interest to match you to a role that you will excel within. As a product manager, you will guide the vision, strategy, and success metrics for your product while working in a cross-functional team that brings together engineering, design, analytics, and commercial expertise. You will translate customer insights, market understanding, and product performance data into clear priorities your team can execute on. You will ensure alignment with stakeholders, drive discovery and delivery activities, and keep a continuous focus on solving meaningful customer problems. This position requires balancing strategic thinking with hands-on execution as you move from concept to launch and ongoing iteration. Who you are • 5+ years of product management experience • Skilled in defining product vision, strategy, and measurable outcomes • Experienced in collaborating with cross-functional teams to drive product development • Strong communication skills with the ability to simplify complex topics • Comfortable prioritising in a fast-moving environment • Knowledgeable in agile and lean development practices • Working proficiency in verbal and written English Awesome to have • Experience with platform, payments, or regulated environments • Familiarity with AI-enhanced product development tools • Ability to engage in technical discussions with engineering partners • Experience in designing or coding Please include a CV in English Curious to learn more about Klarna and what it’s like to work here? Explore our career site!

As a Senior Engineer in Klarna’s General track, you will design, build, and operate scalable, high-performing systems that power our global products. You will take end-to-end ownership of services, from architecture and development to deployment and monitoring, ensuring reliability and performance at scale. Working in small, cross-functional teams, you will collaborate closely with product, design, and other engineering colleagues to solve real customer problems. You will also contribute to technical direction, support continuous improvement of engineering practices, and share knowledge to strengthen the team’s capabilities. Who you are • Proven experience as a software engineer, with strong coding skills in at least one modern programming language • Experience designing and building scalable, distributed systems and working with microservices architectures • Strong understanding of software engineering fundamentals, including testing, code quality, and system design • Experience working in cloud environments and with modern development tools and practices (e.g. CI/CD, containerization) • Ability to take ownership of systems and drive work independently while collaborating effectively with others • Strong problem-solving skills and ability to navigate complex technical challenges • Working proficiency in English, both written and verbal Awesome to have • Experience in fintech, payments, or other high-scale digital products • Familiarity with multiple programming languages and technology stacks • Experience with observability, monitoring, and performance optimization • Experience mentoring engineers and contributing to team development Please include a CV in English

What you’ll do Own the global UW tables (canonical facts/dimensions for applications, decisions, features, repayments, delinquency) with clear SLAs for freshness, completeness, accuracy, and data lineage. Design for AI-agents and humans: consistent IDs, canonical events, explicit metric definitions, rich metadata (schemas, data dictionaries), and machine-readable data contracts. Build & run pipelines (batch + streaming) that feed UW scoring, real-time decisioning, monitoring, and underwriting optimization. Instrument quality & observability (alerts, audits, reconciliation, backfills) and drive incident/root-cause reviews. Partner closely with Credit Portfolio Management, Policy teams, Modeling teams, and treasury and finance teams to land features for RUE and consumer-centric models, plus regulatory and management reporting. Tech stack (what we use) Languages: SQL, PySpark, Python Frameworks: Apache Airflow, AWS Glue, Kafka, Redshift Cloud & DevOps: AWS (S3, Lambda, CloudWatch, SNS/SQS, Kinesis), Terraform; Git; CI/CD What you’ll bring Proven ownership of mission-critical data products (batch + streaming). Data modeling, schema evolution, data contracts, and strong observability chops. Familiarity with AI/agent patterns (agent-friendly schemas/endpoints, embeddings/vector search).

What you’ll do Build and maintain Python and SQL based systems for data pipelines, analyses, monitoring, and automated alerting Evolve Klarna's abilities to measure and monitor its business critical metrics from an AI-driven perspective Own projects end-to-end in time-boxed phases: scoping (1-2 weeks) → building → deploying → iterating Present technical work and tradeoffs to senior leadership Support and maintain what you build – no throwing projects over the wall Work publicly in Slack channels with radical transparency (all work visible to the team) Who you are AI-First Engineer: Heavy hands-on experience with OpenAI/Anthropic APIs, prompt engineering, and AI-assisted coding tools (Cursor, GitHub Copilot, Claude Code, etc) Tech Stack Flexible: Comfortable working across Python, Docker, AWS, APIs, databases – you adapt to the problem, not the other way around Business-Oriented: You translate stakeholder needs into technical solutions and care about why we're solving a problem, not just how Iterative Shipper: You build MVPs in 1-2 week cycles, gather feedback, and iterate quickly Change Resilient: You thrive when priorities shift, requirements evolve, and ambiguity is the norm Self-Driven: You proactively unblock yourself, drive projects forward, and communicate openly in public channels Awesome to have Good understanding of common design patterns and comfortable with modern Python 3 practices (type hints, protocols, Pydantic, uv) Experience with Terraform, Airflow, Slack API, OpenSearch, SPARQL, CI/CD Track record of maintaining production systems while building new features Background working with cross-functional stakeholders in product, analytics, and business operations Comfort working in highly transparent, documentation-heavy environments Experience killing or rescoping projects that aren't delivering value